MODULAR CONTROL SYSTEM FOR ADVANCED AI-ENABLED ELECTRONIC DEVICES

Abstract

ABSTRACT Modular Control System for Advanced AI-Enabled Electronic Devices The present disclosure introduces modular control system for advanced AI-enabled electronic devices 100 designed to enhance system adaptability through modular architecture. It comprises of modular control units 102 that independently manage system functionalities and central AI engine 104, which optimizes performance using machine learning algorithms. Other key components are user interface module 106, communication framework 108, plug-and-play mechanism 110, dynamic AI learning algorithms 112, environmental sensors 114, self-diagnosis and predictive maintenance system 116, data security protocols 118, firmware over-the-Air (OTA) updates 120, adaptive power management system 122, modular AI algorithm integration 124, cross-platform compatibility system 126, remote module configuration system 128, distributed processing capability 130, environmental adaptation features 132, interchangeable modules 134, energy management system 136, real-time environmental monitoring 138, user-centric customization interface 140, user behavior prediction algorithms 142, collaborative device learning mechanism 144, failover and redundancy protocols 146, secure data management protocols 148 and multi-protocol communication framework 150. Reference Fig 1

Specification

Description:Modular Control System for Advanced AI-Enabled Electronic Devices
TECHNICAL FIELD
[0001] The present innovation relates to modular control system for AI-enabled electronic devices, providing enhanced adaptability, scalability, and interoperability across various applications.

BACKGROUND

[0002] The rapid advancement of AI-enabled electronic devices has transformed industries such as smart homes, robotics, and industrial automation. However, many existing control systems in these devices are limited by rigid, monolithic architectures that lack flexibility. These systems are difficult to upgrade or modify without overhauling the entire device, leading to high costs, longer development cycles, and significant electronic waste. Users currently have limited options when it comes to adapting their systems to new requirements or integrating evolving AI technologies. Traditional control systems are often designed as fixed entities, offering limited modularity, and forcing users to discard or replace entire devices when individual components become outdated or need improvement.

[0003] Existing options suffer from key drawbacks, such as the inability to integrate with diverse devices, lack of scalability, and insufficient energy management. Furthermore, non-modular systems result in excessive electronic waste and higher environmental impact, contributing to unsustainable consumption practices. Upgrading or maintaining these systems can be both costly and time-consuming, as it often requires replacing entire systems rather than individual components.

[0004] The invention differentiates itself by introducing a Modular Control System for AI-Enabled Electronic Devices, which offers users the ability to upgrade, customize, and scale their systems through interchangeable modules. These modules can be easily replaced or upgraded, extending the device's lifespan while minimizing electronic waste. The system's central AI engine leverages machine learning to optimize energy usage and improve performance in real-time, addressing the limitations of traditional fixed systems. Key features of the invention include a plug-and-play architecture, real-time environmental adaptation, and support for multiple communication protocols, enabling seamless integration across various devices. The novelty of the invention lies in its modular approach combined with advanced AI capabilities, ensuring both sustainability and adaptability, meeting modern demands for efficient, scalable, and eco-friendly technologies

OBJECTS OF THE INVENTION

[0005] The primary object of the invention is to provide a modular control system for AI-enabled electronic devices, allowing for seamless integration, customization, and scalability.

[0006] Another object of the invention is to enhance device longevity by enabling the replacement and upgrade of individual modules without requiring a complete system overhaul.

[0007] Another object of the invention is to reduce electronic waste by utilizing modular architecture, which allows users to upgrade specific components instead of discarding entire devices.

[0008] Another object of the invention is to improve energy efficiency through real-time AI optimization, reducing power consumption based on environmental conditions and user behavior.

[0009] Another object of the invention is to offer cross-platform compatibility, ensuring that the modular system can be integrated with a wide range of existing devices and systems.

[00010] Another object of the invention is to provide users with a personalized and adaptive control interface, enabling easy configuration and management of the system's modules.

[00011] Another object of the invention is to improve data security by incorporating advanced encryption and authentication protocols, ensuring user data privacy and system integrity.

[00012] Another object of the invention is to enable predictive maintenance by using AI algorithms to monitor system health and detect potential failures before they occur.

[00013] Another object of the invention is to support the sustainable development of electronic devices through the use of recyclable materials and environmentally friendly manufacturing practices.

[00014] Another object of the invention is to provide a versatile system that can be applied across various industries.

SUMMARY OF THE INVENTION

[00015] In accordance with the different aspects of the present invention, modular control system for advanced AI-enabled electronic devices is presented. It is designed to enhance adaptability, scalability, and sustainability across various applications. The system comprises of interchangeable modules that can be easily upgraded, reducing electronic waste and extending device lifespan. A central AI engine optimizes performance and energy efficiency through real-time data analysis and environmental adaptation. The system supports multiple communication protocols, ensuring seamless integration with diverse devices. It offers users customization and predictive maintenance, promoting efficient and sustainable electronic solutions.
[00016] Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow.

[00017] It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.

BRIEF DESCRIPTION OF DRAWINGS
[00018] The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.

[00019] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

[00020] FIG. 1 is component wise drawing for modular control system for advanced AI-enabled electronic devices.

[00021] FIG 2 is working methodology of modular control system for advanced AI-enabled electronic devices.

DETAILED DESCRIPTION

[00022] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.

[00023] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of modular control system for advanced AI-enabled electronic devices and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.

[00024] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[00025] The terms "comprises", "comprising", "include(s)", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by "comprises... a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[00026] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings and which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[00027] The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.

[00028] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of modular control units 102, central AI engine 104, user interface module 106, communication framework 108, plug-and-play mechanism 110, dynamic AI learning algorithms 112, environmental sensors 114, self-diagnosis and predictive maintenance system 116, data security protocols 118, firmware over-the-Air (OTA) updates 120, adaptive power management system 122, modular AI algorithm integration 124, cross-platform compatibility system 126, remote module configuration system 128, distributed processing capability 130, environmental adaptation features 132, interchangeable modules 134, energy management system 136, real-time environmental monitoring 138, user-centric customization interface 140, user behavior prediction algorithms 142, collaborative device learning mechanism 144, failover and redundancy protocols 146, secure data management protocols 148 and multi-protocol communication framework 150.

[00029] Referring to Fig. 1, the present disclosure provides details of modular control system for advanced AI-enabled electronic devices 100 which is designed to enhance scalability, adaptability, and sustainability by integrating modular architecture with advanced AI capabilities. The system comprises of key components such as modular control units 102, central AI engine 104, and user interface module 106, facilitating seamless upgrades and customization. The communication framework 108 allows interoperability across devices, while the plug-and-play mechanism 110 ensures easy integration of new modules. The system also includes dynamic AI learning algorithms 112 to optimize performance and self-diagnosis and predictive maintenance system 116 to ensure reliability. Additionally it is provided with adaptive power management system 122 and real-time environmental monitoring 138 contribute to energy efficiency and device longevity.

[00030] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with modular control units 102, which serve as the core components for managing different functionalities within the system. Each unit operates independently, enabling users to replace or upgrade specific modules without affecting the entire system. These units are designed to be easily integrated through the plug-and-play mechanism 110, allowing seamless addition of new features or capabilities. The modular control units 102 communicate with the central AI engine 104 to receive operational instructions and adapt to real-time data processing needs.

[00031] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with central AI engine 104, which acts as the brain of the system, processing inputs from various sensors and modules. This engine uses advanced machine learning algorithms to optimize device performance, predict user behavior, and manage energy consumption. The central AI engine 104 works closely with the modular control units 102 to ensure efficient operation and adapts based on data provided by the environmental sensors 114. It also coordinates with the user interface module 106 to update users on system performance and allow customization.

[00032] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with user interface module 106, which allows users to interact with the system and configure its settings. This module can be accessed through mobile applications, web interfaces, or voice-activated assistants, offering flexibility in controlling the system. The user interface module 106 communicates with the central AI engine 104 to reflect real-time system data and allows users to modify the behavior of the modular control units 102 based on their preferences. It also receives notifications from the self-diagnosis and predictive maintenance system 116.

[00033] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with communication framework 108, which ensures seamless interaction between the modular control units 102, the central AI engine 104, and external devices. Supporting multiple communication protocols like Wi-Fi, Bluetooth, and Zigbee, the communication framework 108 facilitates data exchange and interoperability across various devices and sensors. This framework plays a key role in maintaining the system's modularity by allowing each component to share data efficiently, making it essential for the real-time adaptability of the system.

[00034] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with plug-and-play mechanism 110, enabling the easy integration of new or upgraded modules into the system without requiring extensive configuration. This mechanism allows users to add components such as additional modular control units 102 or specialized modules like environmental sensors 114 seamlessly. The plug-and-play mechanism 110 enhances the system's flexibility, ensuring that users can upgrade their devices as new technologies or needs arise. It interfaces with the communication framework 108 to automatically detect and incorporate new modules.

[00035] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with dynamic AI learning algorithms 112, which enable the system to continuously optimize its performance based on real-time data and user behavior. These algorithms are integrated within the central AI engine 104 and interact with the modular control units 102 to adapt the system's functionality dynamically. The dynamic AI learning algorithms 112 play a critical role in improving energy efficiency and personalization by learning from user interactions via the user interface module 106.

[00036] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with environmental sensors 114, which monitor external conditions such as temperature, humidity, and light levels. These sensors feed real-time data into the central AI engine 104, which adjusts system operations accordingly to optimize performance. The environmental sensors 114 work closely with the adaptive power management system 122 to ensure that the system operates efficiently based on environmental conditions, reducing unnecessary power consumption.

[00037] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with self-diagnosis and predictive maintenance system 116, which continuously monitors the health and performance of each module in the system. It works by analyzing operational data collected from the modular control units 102 and providing real-time feedback to the central AI engine 104. This system helps identify potential failures before they occur and alerts users through the user interface module 106, enabling proactive maintenance and reducing system downtime.

[00038] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with data security protocols 118, which protect user data and ensure secure communication across all system components. These protocols include encryption, user authentication, and regular software updates to safeguard agAInst cyber threats. The data security protocols 118 interact with the communication framework 108 to ensure that all data exchanged between the modular control units 102 and external devices is securely transmitted.

[00039] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with firmware over-the-Air (OTA) updates 120, which allow users to remotely update the system's software without needing physical access to the devices. This feature ensures that the system can continuously receive new AI algorithms and security patches, maintaining optimal performance. The firmware over-the-Air (OTA) updates 120 work in conjunction with the user interface module 106, allowing users to schedule or manage updates easily.

[00040] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with adaptive power management system 122, which intelligently regulates power consumption based on usage patterns and environmental conditions. This system works by analyzing data from the environmental sensors 114 and optimizing energy distribution to active modules while conserving energy in idle ones. The adaptive power management system 122 interacts with the central AI engine 104 to prioritize power-efficient operations, extending battery life in portable devices and reducing overall energy consumption.

[00041] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with modular AI algorithm integration 124, allowing users to add specialized AI algorithms tailored to specific applications such as voice recognition or predictive analytics. This feature enables the system to adapt to evolving user requirements without the need for a complete overhaul. The modular AI algorithm integration 124 works with the plug-and-play mechanism 110, ensuring that new AI modules are easily incorporated and immediately functional within the system.

[00042] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with cross-platform compatibility system 126, which ensures that the system can operate seamlessly across different operating systems and devices. This component facilitates integration with smart home systems, industrial automation platforms, and robotics, making it highly versatile. The cross-platform compatibility system 126 works closely with the communication framework 108 to ensure smooth data exchange between various platforms, enhancing the system's usability.

[00043] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with remote module configuration system 128, which allows users to manage and configure individual modules from any location via cloud-based services. This system improves accessibility and flexibility, especially for users managing multiple devices in different environments. The remote module configuration system 128 interacts with the user interface module 106, enabling real-time configuration changes and monitoring through a user-friendly dashboard.

[00044] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with distributed processing capability 130, which allows tasks to be processed across multiple modular control units 102 in parallel, improving system efficiency and response time. This feature is particularly useful in applications that require complex data analysis, such as industrial automation or robotics. The distributed processing capability 130 works with the central AI engine 104 to allocate tasks dynamically, ensuring that each module performs optimally.

[00045] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with environmental adaptation features 132, enabling the system to adjust its operations based on real-time environmental data provided by the environmental sensors 114. These features ensure that the system performs efficiently by adapting to changing conditions such as temperature, light, and humidity. The environmental adaptation features 132 work with the adaptive power management system 122 to minimize energy usage during periods of low activity.

[00046] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with interchangeable modules 134, which allow users to customize the system's functionality by easily replacing or upgrading individual components. This modularity extends the system's lifespan and makes it highly adaptable to changing user needs. The interchangeable modules 134 are designed to work seamlessly with the plug-and-play mechanism 110, ensuring that new modules can be installed without any system downtime.

[00047] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with energy management system 136, which optimizes the distribution of power across different modules based on real-time usage data. The system ensures that active modules receive the necessary energy while conserving power for idle components, thereby increasing efficiency. The energy management system 136 works in conjunction with the adaptive power management system 122 to further reduce energy consumption, especially in battery-powered devices.

[00048] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with real-time environmental monitoring 138, which continuously tracks external conditions such as Air quality, temperature, and light levels. This data is fed into the central AI engine 104 to adjust system operations for optimal performance. The real-time environmental monitoring 138 also works with the user interface module 106, allowing users to view environmental data and adjust settings accordingly.

[00049] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with user-centric customization interface 140, which enables users to personalize the settings and functionalities of the system based on their preferences. This component offers a flexible and intuitive way to configure various modular control units 102. The user-centric customization interface 140 communicates with the central AI engine 104, ensuring that personalized settings are applied in real-time and reflected in system operations.

[00050] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with user behavior prediction algorithms 142, which analyze user patterns over time to predict future actions and preferences. These algorithms are integrated into the central AI engine 104 and work with the dynamic AI learning algorithms 112 to enhance personalization and system efficiency. The user behavior prediction algorithms 142 also improve the overall user experience by automatically adjusting system settings to match expected user needs.

[00051] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with collaborative device learning mechanism 144, which allows each modular unit to share insights and learnings with other connected devices. This mechanism enhances the system's overall intelligence by leveraging data from multiple sources, resulting in smarter decision-making. The collaborative device learning mechanism 144 interacts with the central AI engine 104 to continuously improve system performance based on shared data.

[00052] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with failover and redundancy protocols 146, ensuring continuous system operation in the event of a module failure. These protocols automatically reroute tasks to backup modules or alternate processing paths, preventing system downtime. The failover and redundancy protocols 146 are closely integrated with the distributed processing capability 130, allowing for seamless task allocation during failures.

[00053] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with secure data management protocols 148, which safeguard user data through advanced encryption, authentication, and regular security updates. These protocols ensure the integrity and confidentiality of data shared between the modular control units 102 and external devices. The secure data management protocols 148 are essential for maintaining user trust, particularly in applications where sensitive data is processed.

[00054] Referring to Fig. 1, modular control system for advanced AI-enabled electronic devices 100 is provided with multi-protocol communication framework 150, supporting various communication standards such as Wi-Fi, Bluetooth, and Zigbee. This framework ensures that the system can interact seamlessly with a wide range of external devices and sensors. The multi-protocol communication framework 150 works in tandem with the communication framework 108 to provide robust and flexible data transmission capabilities, facilitating interoperability across different platforms and applications.

[00055] Referring to Fig 2, there is illustrated method 200 for modular control system for advanced AI-enabled electronic devices 100. The method comprises:

At step 202, method 200 includes user configuring the modular control units 102 via the user interface module 106 to set up the desired system preferences;

At step 204, method 200 includes the central AI engine 104 receiving input from the environmental sensors 114 to monitor external conditions such as temperature and humidity;

At step 206, method 200 includes the central AI engine 104 processing the data received from the sensors and dynamically adjusting the functionality of the modular control units 102 based on the environmental conditions;

At step 208, method 200 includes the communication framework 108 facilitating data exchange between the modular control units 102 and external devices connected via multiple communication protocols such as Wi-Fi or Bluetooth;

At step 210, method 200 includes the dynamic AI learning algorithms 112 optimizing the energy consumption of the system based on usage patterns and external conditions, ensuring efficient performance;

At step 212, method 200 includes the self-diagnosis and predictive maintenance system 116 monitoring the health of each module and providing real-time feedback to the user interface module 106 in case of any system irregularities;

At step 214, method 200 includes the system executing a firmware over-the-air (OTA) update 120 to integrate the latest AI algorithms and security patches, ensuring the system remains up-to-date without manual intervention;

At step 216, method 200 includes the adaptive power management system 122 adjusting the power distribution to active modules while conserving energy in idle ones, based on data from the environmental sensors 114;

At step 218, method 200 includes the modular AI algorithm integration 124 enhancing specific functionalities, such as voice recognition, based on real-time inputs and the user's needs;

At step 220, method 200 includes the real-time environmental monitoring 138 continuously feeding data into the system, ensuring the system's operation is optimized according to environmental conditions.


[00056] In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms "fixed" "attached" "disposed," "mounted," and "connected" are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.

[00057] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying clAIms. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and clAIm the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.

[00058] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended clAIms. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
, Claims:WE CLAIM:
1. A modular control system for advanced AI-enabled electronic devices 100 comprising of
modular control units 102 to provide independent control and management of specific functionalities within the system;
central AI engine 104 to process data and optimize system performance through machine learning algorithms;
user interface module 106 to allow users to configure and monitor the system settings;
communication framework 108 to facilitate seamless data exchange between devices and external components;
plug-and-play mechanism 110 to enable easy integration and upgrades of new modules without system reconfiguration;
dynamic AI learning algorithms 112 to adapt and optimize energy consumption and system performance based on real-time data;
environmental sensors 114 to monitor external conditions like temperature and humidity for system adaptation;
self-diagnosis and predictive maintenance system 116 to monitor module health and provide real-time feedback;
data security protocols 118 to ensure safe and encrypted communication across all system components;
firmware over-the-air (OTA) updates 120 to remotely update software and integrate new features without manual intervention;
adaptive power management system 122 to intelligently regulate power consumption and improve energy efficiency;
modular AI algorithm integration 124 to enable the addition of specialized AI functionalities such as voice recognition;
cross-platform compatibility system 126 to ensure smooth operation across various devices and platforms;
remote module configuration system 128 to allow users to configure and manage modules remotely via cloud services;
distributed processing capability 130 to allocate tasks dynamically across multiple modules for optimal system performance;
environmental adaptation features 132 to adjust system operations based on changing environmental conditions;
interchangeable modules 134 to enable easy replacement and upgrades of individual components;
energy management system 136 to optimize power distribution across active and idle modules;
real-time environmental monitoring 138 to continuously track and respond to external environmental data;
user-centric customization interface 140 to provide a personalized and intuitive configuration of system settings;
user behavior prediction algorithms 142 to analyze user patterns and optimize system behavior accordingly;
collaborative device learning mechanism 144 to enable devices to share insights and improve overall system intelligence;
failover and redundancy protocols 146 to ensure continuous operation in case of module failures;
secure data management protocols 148 to safeguard user data with encryption and authentication methods; and
multi-protocol communication framework 150 to support various communication standards like Wi-Fi and Bluetooth for seamless connectivity.

2. The modular control system for advanced AI-enabled electronic devices 100, wherein modular control units 102 are configured to manage specific system functionalities independently, enabling seamless upgrades and customization without affecting other components, enhancing system scalability and flexibility.

3. The modular control system for advanced AI-enabled electronic devices 100 as claimed in claim 1, wherein central AI engine 104 is configured to process data from sensors and devices, using machine learning algorithms to optimize system performance, adapt to environmental changes, and manage energy consumption in real-time.

4. The modular control system for advanced AI-enabled electronic devices 100 as claimed in claim 1, wherein user interface module 106 is configured to allow users to configure system settings, monitor performance, and receive updates, offering a personalized experience across multiple platforms such as mobile applications and web interfaces.

5. The modular control system for advanced AI-enabled electronic devices 100 as claimed in claim 1, wherein communication framework 108 is configured to facilitate data exchange between the modular control units 102, central AI engine 104, and external devices, supporting multiple communication protocols such as Wi-Fi, Bluetooth, and Zigbee for interoperability.

6. The modular control system for advanced AI-enabled electronic devices 100 as claimed in claim 1, wherein dynamic AI learning algorithms 112 are configured to optimize system operations based on real-time data and user behavior, continuously improving energy efficiency and performance through machine learning.

7. The modular control system for advanced AI-enabled electronic devices 100 as claimed in claim 1, wherein self-diagnosis and predictive maintenance system 116 is configured to monitor the health and performance of individual modules, providing real-time feedback to users and predicting potential failures for proactive maintenance.

8. The modular control system for advanced AI-enabled electronic devices 100 as claimed in claim 1, wherein adaptive power management system 122 is configured to intelligently regulate power distribution across active and idle modules, optimizing energy usage based on environmental conditions and usage patterns.

9. The modular control system for advanced AI-enabled electronic devices 100 as claimed in claim 1, wherein firmware over-the-air (OTA) updates 120 are configured to remotely deliver software updates and security patches, enabling continuous system enhancement without requiring manual intervention or physical access to devices.

10. The modular control system for advanced AI-enabled electronic devices 100 as claimed in claim 1, wherein method comprises of
modular control units 102 being configured via the user interface module 106 to set up the desired system preferences;
central AI engine 104 receiving input from the environmental sensors 114 to monitor external conditions such as temperature and humidity;
central AI engine 104 processing the data received from the sensors and dynamically adjusting the functionality of the modular control units 102 based on the environmental conditions;
communication framework 108 facilitating data exchange between the modular control units 102 and external devices connected via multiple communication protocols such as Wi-Fi or Bluetooth;
dynamic AI learning algorithms 112 optimizing the energy consumption of the system based on usage patterns and external conditions, ensuring efficient performance;
self-diagnosis and predictive maintenance system 116 monitoring the health of each module and providing real-time feedback to the user interface module 106 in case of any system irregularities;
firmware over-the-air (OTA) update 120 integrating the latest AI algorithms and security patches, ensuring the system remains up-to-date without manual intervention;
adaptive power management system 122 adjusting the power distribution to active modules while conserving energy in idle ones, based on data from the environmental sensors 114;
modular ai algorithm integration 124 enhancing specific functionalities, such as voice recognition, based on real-time inputs and the user's needs; and
real-time environmental monitoring 138 continuously feeding data into the system, ensuring the system's operation is optimized according to environmental conditions.

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